Models in the tropics: Why so uncertain?

As Tropical Storm Isaac spins toward Cuba today, we're still talking about the "uncertainty" in the forecast, not only for track, but for intensity, too. Both models and forecasters are pretty confident in the forecast track over the next day or so, but what Isaac will look like once it reaches the Gulf of Mexico, and what intensity the storm could reach, is still buried in the cards.

Why so uncertain? The most basic answer to this question is that the atmosphere is chaotic, and predicting an outcome in a chaotic system sometimes seems more like a dark art than a science. But the more we know about how the system works, and the physics that govern it, the better our forecasts can be. And we've come along way since the dawn of weather forecasting. I can say with high confidence what the temperature will be tomorrow and whether or not it will rain. However, when it comes to tropical cyclones, especially cyclones like Isaac, there are so many variables at play, and so many that we don't know, that our certainty drops, and this is when its most important to be aware of changes in the forecast if you're in a vulnerable location.

Factors involved in forecast uncertainty in the tropics

• Quality and number of observationsOne of the big reasons day-to-day weather is easier to predict than the path of a hurricane is that we have great observations over land. Weather stations across the country, at airports and in cities and in peoples' backyards, are constantly recording what the weather is. We also launch weather balloons into the air twice a day all over the country, and most of the world. Models use this data to get a "best guess" at what the atmosphere looks like right at this very moment, and then uses that as a starting point for a forecast. Like a runner in the Olympics, if the observations are bad and 3 feet behind the line, that runner isn't going to perform well. Meteorologists have a saying, "junk in, junk out." If we apply this theory to hurricane forecasting, you can see where the problem lies. How do we get frequent, high quality observations of a hurricane over water in the middle of the Atlantic? A couple of ways include visible, infrared, and wind-measuring satellite instruments. We can see a lot from space, but obviously having "ground-truth" observations is ideal. If a ship happens to be heading through a storm that's weak or developing, it can send some data, and if the storm is passing over an island, forecasters there can launch weather balloons into the storm. However, these are just specks of data in a vast ocean basin. This is where the Hurricane Hunters come in, whose job it is to fly into these tropical beasts and record as much data as possible on a single tank of gas. There are also planes (Gulfstreams) that fly above the storm and drop instruments into it, which then relay information on the way down. Models ingest this information just like they ingest data from a weather station, which greatly improves the forecast. Without the Hurricane Hunters, our forecasts would be much more uncertain.

An example of an ensemble model forecast from NCEP. This run happened on Friday, August 24th, at 8am EDT. Each line is a single model run. The circles mark off points in time along the line.

• Weather model chaosYes, I know chaos was the "basic answer" to the question, but there's a little more to it. Given our previous item about observation quality and frequency, we know that in the best case scenario, we want a 100% complete and accurate, three-dimensional view of the atmosphere to start a model with. In the worst case scenario, we have no data at all. Reality falls somewhere in the middle. We know that we're not going to have all the data we need to make a perfect forecast (wouldn't that be nice?). So we use that knowledge to hedge our bets. Instead of just running one single model, and hoping that the model gets it right, we run many, many models. These "ensemble forecasts" assume our data isn't perfect or complete. Some of them are the same model (e.g. the GFS or the ECMWF) run multiple times with slight variations in the initial conditions. Another way to get an ensemble forecast is to run a bunch of different models with the same initial data. We use ensemble forecasts for all kinds of weather, but in the case of tropical cyclones, the result doesn't look like one single line, or one single intensity forecast. It ends up looking something like the National Hurricane Center's "cone of uncertainty." It's basically the models' cone of uncertainty. The individual model runs come together to form a pretty good idea of what's going to happen, along with the "spread" of uncertainty, which translates into risk. And having an accurate picture of your risk is the best way you can prepare for an approaching storm.

• Strength and structure of the stormThis is probably the most interesting and yet most frustrating part of nailing down a tropical cyclone forecast. If a hurricane is well-developed, it's often easier to forecast the final outcome. Well-developed hurricanes are tall in the atmosphere, and their circulation is usually broad and uniform. It makes the "initial picture" easier to paint. For this reason, the models are able to get a handle on the hurricane and forecast an outcome that we're more likely to see than if a storm is disorganized and weak. Isaac has been disorganized for much of this week. The location of the center of circulation at the surface was not in the same place as the strongest thunderstorm activity. We saw the same thing in satellite measurements--that the storm was "tilted" with height--the sign of a poorly organized cyclone. Isaac was also weak. It's circulation was not well-developed and so surface wind speeds remained below hurricane threshold--a good thing for Hispaniola, Puerto Rico, and other affected islands in the Caribbean, to be sure. But this also leads to more uncertainty in the forecast track, and the forecast intensity. A weak storm's center of circulation has an easier time "shifting" from one place to another, and the center location is one of the most important factors in determining the track. We saw Isaac's center shift or wobble a few times over the past few days. In terms of intensity, it's hard to get a good idea of what Isaac will look like after it passes over Cuba, because we don't know exactly what impact Cuba will have on it. We know for sure that mountainous islands weaken tropical cyclones. However, a slight shift to the north or south and Isaac is back over warm water, which is the storm's fuel.

Given all this uncertainty, what should you do to prepare? Pay close attention to the forecasts from the National Hurricane Center. Be aware of the center's cone of uncertainty. Never assume that a cyclone is going to head directly down the middle of that cone. Assess your risk, and have a plan. And of course, be sure to heed weather warnings and follow directives from your local emergency managers.

Nice primer blog Angela, thank you. Varying the ICs within one model makes perfect sense to me, just so you keep the range within the true range of uncertainty due to imperfect knowledge. Otherwise, if too broad you are unrealistically smearing out the solutions, and if too narrow you may miss the "most correct" solution.

When I think about it, choosing the correct and efficient range of ICs in multi-dimensional space for running ensembles seems to be a very complex problem in itself...the issue of orthogonality alone staggers my mind, not being an atmospheric scientist like yourself. Is that selection done with some rigor, or is it more from experience and empirical observation of the results?

What makes less sense is to just take a bunch of different models into consideration, seems like throwing spaghetti on the wall to see what sticks. The differences between the various global models are mostly accidental, right?

For example in my primitive understanding we've mostly thrown out the old NGM models because it was far too computationally inefficient to update the physics, so we moved away from differential formulations solved by finite difference methods to integral formulations solved by gaussian-type quadrature over the triangulated geoid. With these computational improvements combined with vastly more powerful computers we have improved the spatial and temporal resolution and refined the physical models, but it seems that out of the global models only the GFS and the ECMWF are even in the game anymore, with the others lagging behind. Some of the others may be just failed experiments, necessary bumps in the path of progress, but I do not see how the older models contribute much to operational forecasting. Maybe you could elaborate?

Finally, any good references to the mathematical workings of the various models and their differences would be much appreciated.

Thanks Angela, nice post. BTW, that Marathon webcam is a few hundred feet from my house, we're actually in Key Colony Beach and KCB was incorporated decades before the City of Marathon ;>) but a lot more people know about Marathon than KCB - cheers

Any chance you could discuss the models accuracy changes due to recent updates? I know GFS was updated in 2010 to enable predictions at the 60% level out to 7 days with an add-on that went to 9 days. And that presentation suggested GFS was going to have more implemented in it in 2011. I'm certain other models had updates too.

Any chance you could discuss the models accuracy changes due to recent updates? I know GFS was updated in 2010 to enable predictions at the 60% level out to 7 days with an add-on that went to 9 days. And that presentation suggested GFS was going to have more implemented in it in 2011. I'm certain other models had updates too.

Scientific American for August 2012 page 54 "Deadly Rays from Clouds". Quoting the author ..."Meanwhile, to get closer to the action, we and our collaborators have built an aircraft instrument designed to measure gamma rays from thunderstorms. Worry about the dangers of gamma-ray exposure prevents us from flying straight into a storm."

I assume those brave young men that fly into hurricanes know all about that, right? It kinda changes the definition of courage.